Can AI help identify learning gaps before students fall behind

This article is authored by Umakanta Rana, global president & CEO, TutorCloud AI.

A student struggling with algebra in Class 8 may not necessarily have difficulty with the subject itself. The problem could have started much earlier, perhaps with a basic concept in fractions that was never fully understood. Having managed to get through earlier classes by memorising steps, the student may only begin to struggle when the subject becomes more complex. By then, the gap had already widened.

Artificial intelligence (Representative)

This is a challenge that often goes unnoticed in classrooms. Teachers have to complete the syllabus while working with students who have different learning abilities and levels of understanding. Regular tests and examinations help assess performance, but they do not always reveal where a student first went wrong. In many cases, the problem comes to light only after it starts affecting marks or performance in other subjects.

Artificial Intelligence (AI) is bringing a different approach to identifying such difficulties. Rather than relying only on exa6mination results, AI-based learning systems can examine how students respond to questions, identify repeated mistakes and track their understanding across lessons. This can help educators recognise learning gaps earlier and provide support before students struggle with more advanced concepts.

In a classroom of 30 or 40 students, understanding the individual learning needs of every child is not always easy. A student may appear to be keeping up with the class but may have understood only part of what was taught. Periodic assessments offer some indication of progress, but they capture performance at a particular point rather than the entire learning process.

AI-driven learning systems can examine patterns across multiple exercises and lessons. The frequency of incorrect answers, recurring mistakes and changes in performance can offer clues about concepts that a student has not fully understood.

This is where predictive learning differs from conventional remedial teaching. Remedial support generally begins after a student has fallen behind, while predictive learning attempts to identify areas of difficulty before they begin affecting subsequent lessons. The aim is to make academic intervention part of the learning process rather than something that happens only after an examination.

Why personalised learning needs a different approach? Personalised learning is often associated with allowing students to study at their own pace. However, two students completing the same lesson in the same amount of time may have very different levels of understanding.

For instance, two students may get a question on percentages wrong. One may not understand what a percentage represents, while the other may understand the concept but make an error while converting decimals. Giving both students another set of practise questions may not address the actual problem. Learning platforms are increasingly looking at ways to identify the reason behind such mistakes. TutorCloud AI's Continuous Evaluation and Continuous Learning (CECL) framework is designed to assess student understanding as they work through lessons and identify specific areas where they need support. The AI Personal Tutor uses these insights to provide assistance based on the identified learning gap. This could involve revisiting a foundational concept or practising a particular skill. The approach is intended to make additional learning support more relevant to individual requirements.

The teacher's role remains important. While technology can identify patterns in student performance, it cannot always explain why a student has made a particular mistake. An incorrect answer could indicate a lack of understanding, but it could also be the result of distraction or misreading a question.

Teachers therefore remain central to the process. AI-generated insights can help them identify students who may need additional attention, but interpreting those insights and deciding how to respond requires human judgement.

The same information can also help parents and schools understand where students are facing difficulties. If several students struggle with the same concept, it may be worth revisiting how that topic is being taught rather than treating each case as an individual problem.

There are also questions around the collection and use of student data. Such systems need appropriate safeguards, consent and clear limits on how information is used. Predictions about a student's academic performance should not become labels or restrict their opportunities to learn.

The usefulness of AI in education will depend on how effectively it supports everyday teaching. Identifying a learning gap is only one part of the process. Students must also receive the right guidance at the right time. If a difficulty is addressed only after it affects examination results, much of the opportunity for early intervention has already been missed. AI could help change that by giving teachers a clearer view of student progress while learning is still underway.

(The views expressed are personal)

This article is authored by Umakanta Rana, global president & CEO, TutorCloud AI.